%Structure:
%
%Intro
%	Problems
%		low freq
%		long offsets
%	Solutions
%		Methods
%			new data
%			misfit
%			conditioning
%			regularization
%			extrapolation
%		Non of these use well-logs directly
%			Our method is to ...
%			
%	The paper is organized as follows
%		1st regularity of sampling
%		2nd data generation
%		3rd neural net
%		4th single cmp
%		5th multi cmp
%		6th domain applicability
%		7th conclusions
%		
%
%Regularity and relevance of seismic data
%	seismic survey - regular spacing src rec
%	cmp
%	what it means
%	regular works on simple models
%	fwi works on complex but doesnt use regularity
%	data-driven
%	
%	Relevant data
%		which data contribute to which sub point?
%		standard velocity analysis - hor layered media
%		crs - curved reflectors
%		we propose
%		
%%	Exploiting the regularity of sampling
%	
%Data
%	what are the data pairs? pic {inp connect out}
%	explain inp and out and what has to be related
%	field data not available
%	data driven solution, numerous data
%	has to be generated
%	
%	Realistic random models
%		explain generation engine
%		is it diverse?
%	Modeling
%		any solver
%		how many shots?
%	
%Deep learning framework
%	supervised learning
%	data pairs, spatial dependency = convolutional
%	Preprocessing
%		explain preprocessing
%	explaing perks of conv 
%	keras tensorflow

%
%Single CMP to single log
%	Training dataset fitting
%	Generalization tests
%	Guiding model
%	Mode model deformations
%	Different for the most part horizontally layered model
%	Failure
%	
%Multiple CMP gathers to a single log
%
%Domain applicability
%
%Conclusions
%
%Acknowledgments